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The Relationship Between Learning Styles and Cognitive Traits

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The Relationship Between Learning Styles and Cognitive Traits The Relationship between Learning Styles and Cognitive Traits - Getting Additional Information for Improving Student Modelling Sabine Graf a, Taiyu Linb, Kinshukc aWomen's Postgraduate College for Internet Technologies, Vienna University of Technology, Vienna, Austria bDepartment of Information Systems, Massey University, Palmerston North, New Zealand cSchool of Computing & Information Systems, Athabasca University, Canada Abstract Student modelling is an important process for adaptive virtual learning environments. Student models include a range of information about the learners such as their domain competence, learning style or cognitive traits. To be able to adapt to the learners’ needs in an appropriate way, a reliable student model is necessary, but getting enough information about a learner is quite challenging. Therefore, mechanisms are needed to support the detection process of the required information. In this paper, we investigate the relationship between learning styles, in particular, those pertaining to the Felder-Silverman learning style model and working memory capacity, one of the cognitive traits included in the Cognitive Trait Model. The identified relationship is derived from links between learning styles, cognitive styles, and working memory capacity which are based on studies from literature. As a result, we demonstrate that learners with high working memory capacity tend to prefer a reflective, intuitive, and sequential learning style whereas learners with low working memory capacity tend to prefer an active, sensing, visual, and global learning style. This interaction can be used to improve the student model. Systems which are able to detect either only cognitive traits or only learning styles retrieve additional information through the identified relationship. Otherwise, for systems that already incorporate learning styles and cognitive traits, the interaction can be used to improve the detection process of both by including the additional information of a learning style into the detection process of cognitive traits and vice versa. This leads to a more reliable student model. Keywords Felder-Silverman learning style model, cognitive trait model, working memory capacity, student model 1 1. Introduction Student models (for example, see Brusilovsky, 1994) are essential to any adaptive virtual learning environments. These models contain information about learners such as personal data, domain competence, learning style and cognitive traits, and use this information to adapt to the learners’ needs. An important task for such adaptive environments is to build a robust student model in order to be able to provide adaptivity in an appropriate way, but filling the student model with proper information about the learner is quite challenging. The simplest approach to construct a student model is to ask a student for relevant data. However, this approach is not suitable for identifying accurate information for a number of components of a student model, such as cognitive traits, domain competence, and preferred learning styles. For example, the estimation of domain competence is subjective. To determine cognitive traits and learning styles, comprehensive tests or questionnaire-based surveys are the ordinary means used but these are time consuming and hardly definitive. An alternative approach to collect the information pertinent to a student model is to track the student’s behaviour and responses and then make inferences about general domain competence, cognitive traits, and learning styles. The challenge of this approach is to identify and collect sufficient information to make reliable and useful inferences. To support the detection process of required information, it is beneficial to find mechanisms that use whatever information about the learner is already available to obtain as much reliable information as possible to build a more robust student model. The aim of this paper is to demonstrate the relationship between the learning style and the cognitive traits of a learner. The identified relationship provides additional information which can be used to improve the detection process of both, the learning style and the cognitive traits, in an adaptive virtual learning environment. To exemplify this relationship, we investigate the interaction of working memory capacity, one cognitive trait included in the Cognitive Trait Model (Lin, Kinshuk, and Patel, 2003), with Felder-Silverman learning style model (Felder and Silverman, 1988). Both models as well as their possible implementation in adaptive virtual learning environments are described in the next section in more detail. In Section 3, we present the mapping between the Felder-Silverman learning style model and working memory capacity. This mapping is 2 derived from links between learning styles, cognitive styles, and working memory capacity which are based on studies from literature. Section 4 points out the results as well as the benefits of the identified relationship. Section 5 then concludes the paper. 2. Description of the Learning Style Model and the Cognitive Trait Model In this section, two models - the Felder-Silverman learning style model (FSLSM) and the Cognitive Trait Model (CTM) - are explained to provide background information for the current investigation. While several learning style theories exist in literature, for example, the learning style model by Kolb (1984) and Honey and Mumford (1982), FSLSM seems to be most appropriate for the use in educational systems. Most other learning style models classify learners as belonging to a few groups, whereas Felder and Silverman describe the learning style of a learner in more detail, distinguishing between preferences on four dimensions. Another main issue is that FSLSM is based on tendencies, indicating that learners with a high preference for a certain behaviour can act sometimes differently. The description of FSLSM focuses on the different dimensions as well as the characteristic behaviour and preferences of learners for each dimension. The use of this learning style model in virtual learning environments is briefly discussed, focusing on how to approach filling the student model with information about the learning style. Regarding the Cognitive Trait Model, an insight into how the model can be used and how cognitive traits can be identified in virtual learning environments is provided. 2.1 The Felder-Silverman Learning Style Model Felder-Silverman learning style model (Felder and Silverman, 1988) characterizes each learner according to four dimensions. The first dimension distinguishes between an active and a reflective way of processing information. Active learners learn best by working actively with the learning material, for example, working in groups, discussing the material, or applying it. In contrast, reflective learners prefer to think about and reflect on the material. 3 The second dimension covers sensing versus intuitive learning. Learners with a sensing learning style like to learn facts and concrete learning material, using their sensory experiences of particular instances as a primary source. They tend to be more patient with details and also more practical than intuitive learners and like to relate the learned material to the real world. Intuitive learners prefer to learn abstract learning material, such as theories and their underlying meanings, with general principles rather than concrete instances being a preferred source of information. They like to discover possibilities and relationships and tend to be more innovative and creative than sensing learners. Therefore, they score better in open-ended tests than in tests with a single answer to a problem. This dimension differs from the active-reflective dimension in an important way: the sensing-intuitive dimension deals with the preferred source of information whereas the active-reflective dimension covers the process of transforming the perceived information into knowledge. The third, visual-verbal dimension deals with the preferred input mode. The dimension differentiates learners who remember best what they have seen, e.g. pictures, diagrams and flow-charts, from learners who get more out of textual representations, regardless of the fact whether they are written or spoken. In the fourth dimension, the learners are characterized according to their understanding. Sequential learners learn in small incremental steps and therefore have a linear learning progress. They tend to follow logical stepwise paths in finding solutions. In contrast, global learners use an holistic thinking process and learn in large leaps. They tend to absorb learning material almost randomly without seeing connections but after they have learned enough material they suddenly get the whole picture. Then they are able to solve complex problems and put things together in novel ways but they have difficulties in explaining how they did it. Each learner has a preference in each of the four dimensions. These preferences are expressed by values between +11 to -11 per dimension (Felder and Soloman, 1997). Using the active-reflective dimension as an example, the value +11 means that a learner has a strong preference for active learning, whereas the value -11 states that a learner has a strong preference for reflective learning. Therefore, Felder-Silverman learning style model characterizes each learner by four values between +11 and -11, one for each dimension. For identifying the learning style of learners, Felder and Soloman (1997) developed a questionnaire,
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